Struct HiddenMarkovModel 

pub struct HiddenMarkovModel<State = Untrained> { /* private fields */ }

Hidden Markov Model for sequence modeling

A statistical model that assumes the system being modeled is a Markov process with unobserved (hidden) states. HMMs are particularly useful for temporal pattern recognition such as speech recognition, handwriting recognition, gesture recognition, part-of-speech tagging, and bioinformatics.

Examples

use sklears_core::traits::{Predict, Fit};
use sklears_multioutput::HiddenMarkovModel;
// Use SciRS2-Core for arrays and random number generation (SciRS2 Policy)
use scirs2_core::ndarray::array;

let X = array![[[1.0, 2.0], [2.0, 3.0], [1.5, 2.5]]]; // One sequence
let y = array![[0, 1, 0]]; // State sequence

let hmm = HiddenMarkovModel::new().n_states(2).max_iterations(100);
let trained_hmm = hmm.fit(&X, &y).unwrap();
let predictions = trained_hmm.predict(&X).unwrap();

Implementations

impl HiddenMarkovModel<Untrained>

pub fn new() -> Self

Create a new Hidden Markov Model

pub fn n_states(self, n_states: usize) -> Self

Set the number of hidden states

pub fn max_iterations(self, max_iterations: usize) -> Self

Set the maximum number of iterations for EM algorithm

pub fn tolerance(self, tolerance: Float) -> Self

Set the convergence tolerance

pub fn random_state(self, random_state: u64) -> Self

Set the random state for reproducible results

impl HiddenMarkovModel<Untrained>

pub fn transition_matrix(&self) -> Option<&Array2<Float>>

Get the transition matrix (only available after training)

pub fn emission_means(&self) -> Option<&Array2<Float>>

Get the emission means (only available after training)

pub fn initial_probabilities(&self) -> Option<&Array1<Float>>

Get the initial state probabilities (only available after training)

impl HiddenMarkovModel<HiddenMarkovModelTrained>

pub fn transition_matrix(&self) -> &Array2<Float>

Get the transition matrix

pub fn emission_means(&self) -> &Array2<Float>

Get the emission means

pub fn initial_probabilities(&self) -> &Array1<Float>

Get the initial state probabilities

Trait Implementations

impl<State: Clone> Clone for HiddenMarkovModel<State>

fn clone(&self) -> HiddenMarkovModel<State>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<State: Debug> Debug for HiddenMarkovModel<State>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for HiddenMarkovModel<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for HiddenMarkovModel<Untrained>

type Config = ()

Configuration type for the estimator

type Error = SklearsError

Error type for the estimator

type Float = f64

The numeric type used by this estimator

fn config(&self) -> &Self::Config

Get estimator configuration

fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context

fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

impl Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 3]>>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 2]>>> for HiddenMarkovModel<Untrained>

type Fitted = HiddenMarkovModel<HiddenMarkovModelTrained>

The fitted model type

fn fit(self, X: &Array3<Float>, y: &Array2<i32>) -> SklResult<Self::Fitted>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping

impl Predict<ArrayBase<OwnedRepr<f64>, Dim<[usize; 3]>>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 2]>>> for HiddenMarkovModel<HiddenMarkovModelTrained>

fn predict(&self, X: &Array3<Float>) -> SklResult<Array2<i32>>

Make predictions on the provided data

fn predict_with_uncertainty( &self, x: &X, ) -> Result<(Output, UncertaintyMeasure), SklearsError>

Make predictions with confidence intervals